Top Ten Big Data Security and Privacy Challenges
November2012
? 2012 Cloud Security Alliance
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Contents
Acknowledgments (4)
1.0 Abstract (5)
2.0 Introduction (5)
3.0 Secure Computations in Distributed Programming Frameworks (6)
3.1 Use Cases (6)
4.0 Security Best Practices for Non-Relational Data Stores (6)
4.1 Use Cases (6)
5.0 Secure Data Storage and Transactions Logs (7)
5.1 Use Cases (7)
6.0 End-Point Input Validation/Filtering (7)
6.1 Use Cases (7)
7.0 Real-time Security/Compliance Monitoring (7)
7.1 Use Cases (8)
8.0 Scalable and Composable Privacy-Preserving Data Mining and Analytics (8)
8.1 Use Cases (8)
9.0 Cryptographically Enforced Access Control and Secure Communication (9)
9.1 Use Cases (9)
10.0 Granular Access Control (9)
10.1 Use Cases (9)
11.0 Granular Audits (10)
11.1 Use Cases (10)
12.0 Data Provenance (10)
12.1 Use Cases (10)
13.0 Conclusion (11)
Acknowledgments CSA Big Data Working Group Co-Chairs Lead: Sreeranga Rajan, Fujitsu
Co-Chair: Wilco van Ginkel, Verizon
Co-Chair: Neel Sundaresan, eBay Contributors
Alvaro Cardenas Mora, Fujitsu
Yu Chen, SUNY Binghamton
Adam Fuchs, Sqrrl
Adrian Lane, Securosis
Rongxing Lu, University of Waterloo Pratyusa Manadhata, HP Labs
Jesus Molina, Fujitsu
Praveen Murthy, Fujitsu
Arnab Roy, Fujitsu
Shiju Sathyadevan, Amrita University CSA Global Staff
Aaron Alva, Graduate Research Intern Luciano JR Santos, Research Director Evan Scoboria, Webmaster
Kendall Scoboria, Graphic Designer John Yeoh, Research Analyst
1.0Abstract
Security and privacy issues are magnified by velocity, volume, and variety of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, streaming nature of data acquisition,and high volume inter-cloud migration. Therefore, traditional security mechanisms, which are tailored to securing small-scale static (as opposed to streaming) data, are inadequate.In this paper,we highlight top ten big data-specific security and privacy challenges. Our expectation from highlighting the challenges is that it will bring renewed focus on fortifying big data infrastructures.
2.0Introduction
The term big data refers to the massive amounts of digital information companies and governments collect about us and our surroundings. Every day, we create 2.5 quintillion bytes of data—so much that 90% of the data in the world today has been created in the last two years alone. Security and privacy issues are magnified by velocity, volume, and variety of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, streaming nature of data acquisition and high volume inter-cloud migration. The use of large scale cloud infrastructures, with a diversity of software platforms, spread across large networks of computers, also increases the attack surface of the entire system
Traditional security mechanisms, which are tailored to securing small-scale static (as opposed to streaming) data, are inadequate. For example, analytics for anomaly detection would generate too many outliers. Similarly, it is not clear how to retrofit provenance in existing cloud infrastructures. Streaming data demands ultra-fast response times from security and privacy solutions.
In this paper, we highlight the top ten big data specific security and privacy challenges. We interviewed Cloud Security Alliance members and surveyed security practitioner-oriented trade journals to draft an initial list of high-priority security and privacy problems, studied published research,and arrived at the following top ten challenges:
1.Secure computations in distributed programming frameworks
2.Security best practices for non-relational data stores
3.Secure data storage and transactions logs
4.End-point input validation/filtering
5.Real-time security/compliance monitoring
6.Scalable and composable privacy-preserving data mining and analytics
7.Cryptographically enforced access control and secure communication
8.Granular access control
9.Granular audits
10.Data provenance
In the rest of the paper, we provide brief descriptions and narrate use cases.
3.0Secure Computations in Distributed Programming Frameworks
Distributed programming frameworks utilize parallelism in computation and storage to process massive amounts of data. A popular example is the MapReduce framework,which splits an input file into multiple chunks. In the first phase of MapReduce, a Mapper for each chunk reads the data, performs some computation, and outputs a list of key/value pairs. In the next phase, a Reducer combines the values belonging to each distinct key and outputs the result. There are two major attack prevention measures: securing the mappers and securing the data in the presence of an untrusted mapper.
3.1Use Cases
Untrusted mappers could return wrong results, which will in turn generate incorrect aggregate results. With large data sets, it is next to impossible to identify,resulting in significant damage, especially for scientific and financial computations.
Retailer consumer data is often analyzed by marketing agencies for targeted advertising or customer-segmenting. These tasks involve highly parallel computations over large data sets, and are particularly suited for MapReduce frameworks such as Hadoop. However, the data mappers may contain intentional or unintentional leakages. For example, a mapper may emit a very unique value by analyzing a private record, undermining users’ privacy.
4.0Security Best Practices for Non-Relational Data Stores
Non-relational data stores popularized by NoSQL databases are still evolving with respect to security infrastructure. For instance, robust solutions to NoSQL injection are still not mature. Each NoSQL DBs were built to tackle different challenges posed by the analytics world and hence security was never part of the model at any point of its design stage. Developers using NoSQL databases usually embed security in the middleware. NoSQL databases do not provide any support for enforcing it explicitly in the database. However, clustering aspect of NoSQL databases poses additional challenges to the robustness of such security practices.
4.1Use Cases
Companies dealing with big unstructured data sets may benefit by migrating from a traditional relational database to a NoSQL database in terms of accommodating/processing huge volume of data. In general, the security philosophy of NoSQL databases relies in external enforcing mechanisms. To reduce security incidents, the company must review security policies for the middleware adding items to its engine and at the same time toughen NoSQL database itself to match its counterpart RDBs without compromising on its operational features.
5.0Secure Data Storage and Transactions Logs
Data and transaction logs are stored in multi-tiered storage media. Manually moving data between tiers gives the IT manager direct control over exactly what data is moved and when. However, as the size of data set has been, and continues to be,growing exponentially, scalability and availability have necessitated auto-tiering for big data storage management. Auto-tiering solutions do not keep track of where the data is stored,which poses new challenges to secure data storage. New mechanisms are imperative to thwart unauthorized access and maintain the 24/7 availability.
5.1Use Cases
A manufacturer wants to integrate data from different divisions. Some of this data is rarely retrieved, while some divisions constantly utilize the same data pools. An auto-tier storage system will save the manufacturer money by pulling the rarely utilized data to a lower (and cheaper) tier. However, this data may consist in R&D results, not popular but containing critical information. As lower-tier often provides decreased security, the company should study carefully tiering strategies.
6.0End-Point Input Validation/Filtering
Many big data use cases in enterprise settings require data collection from many sources,such as end-point devices. For example, a security information and event management system (SIEM) may collect event logs from millions of hardware devices and software applications in an enterprise network. A key challenge in the data collection process is input validation: how can we trust the data? How can we validate that a source of input data is not malicious and how can we filter malicious input from our collection? Input validation and filtering is a daunting challenge posed by untrusted input sources, especially with the bring your own device (BYOD) model.
6.1Use Cases
Both data retrieved from weather sensors and feedback votes sent by an iPhone application share a similar validation problem.A motivated adversary may be able to create “rogue” virtual sensors, or spoof iPhone IDs to rig the results. This is further complicated by the amount of data collected, which may exceed millions of readings/votes. To perform these tasks effectively,algorithms need to be created to validate the input for large data sets.
7.0Real-time Security/Compliance Monitoring
Real-time security monitoring has always been a challenge, given the number of alerts generated by (security) devices.These alerts (correlated or not)lead to many false positives, which are mostly ignored or simply “clicked away,”as humans cannot cope with the shear amount. This problem might even increase with big data,
given the volume and velocity of data streams. However, big data technologies might also provide an opportunity, in the sense that these technologies do allow for fast processing and analytics of different types of data.Which in its turn can be used to provide, for instance, real-time anomaly detection based on scalable security analytics.
7.1Use Cases
Most industries and government (agencies) will benefit from real-time security analytics, although the use cases may differ. There are use cases which are common, like,“Who is accessing which data from which resource at what time”; “Are we under attack?” or “Do we have a breach of compliance standard C because of action A?”These are not really new, but the difference is that we have more data at our disposal to make faster and better decisions(e.g.,less false positives) in that regard. However, new use cases can be defined or we can redefine existing use cases in lieu of big data.For example, the health industry largely benefits from big data technologies, potentially saving billions to the tax-payer, becoming more accurate with the payment of claims and reducing the fraud related to claims. However, at the same time, the records stored may be extremely sensitive and have to be compliant with HIPAA or regional/local regulations, which call for careful protection of that same data. Detecting in real-time the anomalous retrieval of personal information, intentional or unintentional, allows the health care provider to timely repair the damage created and to prevent further misuse.
8.0Scalable and Composable Privacy-Preserving Data Mining and Analytics
Big data can be seen as a troubling manifestation of Big Brother by potentially enabling invasions of privacy, invasive marketing, decreased civil freedoms, and increase state and corporate control.
A recent analysis of how companies are leveraging data analytics for marketing purposes identified an example of how a retailer was able to identify that a teenager was pregnant before her father knew. Similarly, anonymizing data for analytics is not enough to maintain user privacy. For example, AOL released anonymized search logs for academic purposes, but users were easily identified by their searchers. Netflix faced a similar problem when users of their anonymized data set were identified by correlating their Netflix movie scores with IMD
B scores.
Therefore, it is important to establish guidelines and recommendations for preventing inadvertent privacy disclosures.
8.1Use Cases
User data collected by companies and government agencies are constantly mined and analyzed by inside analysts and also potentially outside contractors or business partners. A malicious insider or untrusted partner can abuse these datasets and extract private information from customers.
Similarly, intelligence agencies require the collection of vast amounts of data. The data sources are numerous and may include chat-rooms, personal blogs and network routers. Most collected data is, however, innocent in nature, need not be retained,and anonymity preserved.
Robust and scalable privacy preserving mining algorithms will increase the chances of collecting relevant information to increase user safety.
9.0Cryptographically Enforced Access Control and Secure Communication
To ensure that the most sensitive private data is end-to-end secure and only accessible to the authorized entities, data has to be encrypted based on access control policies. Specific research in this area such as attribute-based encryption (ABE) has to be made richer, more efficient,and scalable. To ensure authentication, agreement and fairness among the distributed entities, a cryptographically secure communication framework has to be implemented.
9.1Use Cases
Sensitive data is routinely stored unencrypted in the cloud. The main problem to encrypt data, especially large data sets, is the all-or-nothing retrieval policy of encrypted data, disallowing users to easily perform fine grained actions such as sharing records or searches. ABE alleviates this problem by utilizing a public key cryptosystem where attributes related to the data encrypted serve to unlock the keys. On the other hand, we have unencrypted less sensitive data as well, such as data useful for analytics.Such data has to be communicated in a secure and agreed-upon way using a cryptographically secure communication framework.
10.0Granular Access Control
The security property that matters from the perspective of access control is secrecy—preventing access to data by people that should not have access. The problem with course-grained access mechanisms is that data that could otherwise be shared is often swept into a more restrictive category to guarantee sound security. Granular access control gives data managers a scalpel instead of a sword to share data as much as possible without compromising secrecy.
10.1Use Cases
Big data analysis and cloud computing are increasingly focused on handling diverse data sets, both in terms of variety of schemas and variety of security requirements. Legal and policy restrictions on data come from numerous sources. The Sarbanes-Oxley Act levees requirements to protect corporate financial information, and the Health Insurance Portability and Accountability Act includes numerous restrictions on sharing personal health records. Executive Order 13526 outlines an elaborate system of protecting national security information.
Privacy policies, sharing agreements, and corporate policy also impose requirements on data handling. Managing this plethora of restrictions has so far resulted in increased costs for developing applications and a walled garden approach in which few people can participate in the analysis. Granular access control is necessary for analytical systems to adapt to this increasingly complex security environment.
11.0Granular Audits
With real-time security monitoring (see section 12.0),we try to be notified at the moment an attack takes place. In reality,this will not always be the case (e.g.,new attacks, missed true positives).In order to get to the bottom of a missed attack, we need audit information. This is not only relevant because we want to understand what happened and what went wrong, but also because compliance, regulation and forensics reasons.In that regard, auditing is not something new, but the scope and granularity might be different. For example, we have to deal with more data objects, which probably are (but not necessarily) distributed.
11.1Use Cases
Compliance requirements (e.g., HIPAA, PCI, Sarbanes-Oxley) require financial firms to provide granular auditing records.Additionally, the loss of records containing private information is estimated at $200/record.Legal action –depending on the geographic region –might follow in case of a data breach.Key personnel at financial institutions require access to large data sets containing PI, such as SSN. Marketing firms want access, for instance,to personal social media information to optimize their customer-centric approach regarding online ads.
12.0Data Provenance
Provenance metadata will grow in complexity due to large provenance graphs generated from provenance-enabled programming environments in big data applications.Analysis of such large provenance graphs to detect metadata dependencies for security/confidentiality applications is computationally intensive.
12.1Use Cases
Several key security applications require the history of a digital record –such as details about its creation. Examples include detecting insider trading for financial companies or to determine the accuracy of the data source for research investigations.These security assessments are time sensitive in nature, and require fast algorithms to handle the provenance metadata containing this information. In addition,data provenance complements audit logs for compliance requirements, such as PCI or Sarbanes-Oxley.
13.0Conclusion
Big data is here to stay.It is practically impossible to imagine the next application without it consuming data, producing new forms of data, and containing data-driven algorithms. As compute environments become cheaper, applications environments become networked, and system and analytics environments become shared over the cloud, security, access control, compression and encryption and compliance introduce challenges that have to be addressed in a systematic way. The Cloud Security Alliance (CSA) Big Data Working Group (BDWG) recognizes these challenges and has a mission for addressing these in a standardized and systematic way.
In this paper,we have highlighted the top ten security and privacy problems that need to be addressed for making big data processing and computing infrastructure more secure. Some common elements in this list of top ten issues that are specific to big data arise from the use of multiple infrastructure tiers (both storage and computing) for processing big data, the use of new compute infrastructures such as NoSQL databases (for fast throughput necessitated by big data volumes) that have not been thoroughly vetted for security issues, the non-scalability of encryption for large data sets, non-scalability of real-time monitoring techniques that might be practical for smaller volumes of data, the heterogeneity of devices that produce the data, and confusion with the plethora of diverse legal and policy restrictions that leads to ad hoc approaches for ensuring security and privacy. Many of the items in the list of top ten challenges also serve to clarify specific aspects of the attack surface of the entire big data processing infrastructure that should be analyzed for these types of threats. We plan to use OpenMobius, an open-source,large scale,distributed data processing, analytics,and tools platform from eBay Research Labs as an experimental test bed.
Our hope is that this paper will spur action in the research and development community to collaboratively increase focus on the top ten challenges,leading to greater security and privacy in big data platforms.
安全生产工作存在的问题及下步打算 一、工作开展情况 1、狠抓隐患排查,强化事故防范。按照“全覆盖、零容忍、严执法、重实效”的基本要求,构建安全生产隐患大排查大整治大执法大督查长效机制。(1)开展大排查。安监办联合**等部门对辖区存在安全隐患的单位进行了大排查,做到有隐患记录登记、有企业负责人签字、有整改措施要求、有整改时间限制,全面摸清了安全隐患与薄弱环节,实现排查“全覆盖”,共查出安全隐患*处。目前已整改*处,正在整改*处。(2)开展大执法。在非法加油点整治活动中,坚持依法依规、注重实效与“四不放过”的原则,联合**等执法部门严厉打击非法加油点违法经营行为,实现“严执法”。对辖区*个非法加油点依法经行了取缔。 2、狠抓专项整治,强化重点监管。结合季节特点与行业特点,全方位、全过程深化重点行业领域以及重点敏感时期专项整治活动。危险化学品安全,加强对**等*家危化产品的储存、经营、运输、废弃物处置等环节的安全检查,强化了“两重点一重大”(重点监管危险化工工艺、重点监管危险化学品、重大危险源)的安全监管。烟花爆竹安全,对辖区内*家烟花爆竹经营户进行了督查,强化了生产、销售、储存、运输、燃放等环节的监管,打击了非法违法、违章违规行为。
人员密集场所安全,突出抓好了学校、超市、“三合一”、“九小场所”等公众聚集场所的消防安全、电气电路安全,确保了疏散通道、消防通道、救援通道的畅通。食品集中整治,以“双创”为契机,对辖区餐饮类、生产加工类、食品流通类等*余家食品生产经营单位进行了集中整治,消除了食品 安全隐患,确保不发生食品安全事故。 二、当前安全生产工作存在的主要问题 (一)安全生产基础薄弱。一就是安全生产基础非常脆弱。一些行业、领域与地区安全状况比较严峻,生产装置、设备老化,加之维修与更新改造不落实,管理混乱,安全保障能力低下。二就是长期安全投入不足。特别就是化工企业,不仅就是安全投入不足,基础产业更就是投入不足。三就是安全科技进步进展缓慢。点多面广、规模小、工艺落后、安全生产条件差的状况未得到根本改变,相当部分企业的安全生产条件仍达不到国家规定要求,安全科技进步进展不快。 (二)安全生产意识淡漠。意识决定行为,行为产生后果。许多事故的发生,都就是由于安全意识淡漠,安全知识缺乏,安全行为不规范,违规违章所造成的。一就是企业安全给效益让路。企业为追求经济利益,轻安全生产,不严格执行安全生产法律法规规定的安全生产保障措施,把一切精力用在片面追求任务的完成、效益的最大化上。二就是从业人员安全意识差。从业人员安全生产防范意识较为淡薄,缺乏必要的
1.Please don't see me off.The journey I'm walking on alone is lonely and dangerous. 请不要为我送行。我即将独自踏上的旅途是孤独且布满荆棘的。 2.I will always keep my eyes wide open so that I can know everything in your heart. 我会一直睁大眼睛这样的话我就能读出你心底的一切了。
3.I love the way of rain drops falling on the leaves because that is the way you loved me . 我喜欢雨滴落在树叶上的方式因为你也曾经这样爱过我。
4.I miss you. I miss you. I miss you. Even if let me say this one thousand times,I will never get tired of it. 我想你我想你我想你呀即使让我说一千遍我也永远不会厌倦。
5.Look at the stars in the sky,that's all my wishes especiallly for you. 你看到夜空中的星星了么那都是专属于你我的祝福啊。 6.Yep.I'm wondering if you will give all yourself to me when I need you. 是的。我在想我需要你的时候你会不会把全部的你托付给我。
大数据安全的六大挑战 大数据的价值为大家公认。业界通常以4个“V”来概括大数据的基本特征——Volume(数据体量巨大)、Variety(数据类型繁多)、Value(价值密度低)、Velocity(处理速度快)。当你准备对大数据所带来的各种光鲜机遇大加利用的同时，请别忘记大数据也会引入新的安全威胁，存在于大数据时代“潘多拉魔盒”中的魔鬼可能会随时出现。 挑战一：大数据的巨大体量使得信息管理成本显著增加 4个“V”中的第一个“V”(Volume)，描述了大数据之大，这些巨大、海量数据的管理问题是对每一个大数据运营者的最大挑战。在网络空间，大数据是更容易被“发现”的显著目标，大数据成为网络攻击的第一演兵场所。一方面，大量数据的集中存储增加了泄露风险，黑客的一次成功攻击能获得比以往更多的数据量，无形中降低了黑客的进攻成本，增加了“攻击收益”;另一方面，大数据意味着海量数据的汇集，这里面蕴藏着更复杂、更敏感、价值巨大的数据，这些数据会引来更多的潜在攻击者。 在大数据的消费者方面，公司在未来几年将处理更多的内部生成的数据。然而在许多组织中，不同的部门像财务、工程、生产、市场、IT等之间的信息仍然是孤立的，各部门之间相互设防，造成信息无法共享。那些能够在不破坏壁垒和部门现实优势的前提下更透明地沟通的公司将更具竞争优势。 【解决方案】首先要找到有安全管理经验并受过大数据管理所需要技能培训的人员，尤其是在今天人力成本和培训成本不断上升的节奏中，这一定足以让许多CEO肝颤，但这些针对大数据管理人员的巨额教育和培训成本，是一种非常必要的开销。 与此同时，在流程的设计上，一定要将数据分散存储，任何一个存储单元被“黑客”攻破，都不可能拿到全集，同时对于不同安全域要进行准确的评估，像关键信息索引的保护一定要加强，“好钢用在刀刃上”，作为数据保全，能够应对部分设施的灾难性损毁。 挑战二：大数据的繁多类型使得信息有效性验证工作大大增加
I don’t understand why fate brings two people who can’t stay together forever to each other. 我不明白，为什么命运要让两个不可能在一起的人相遇。 I’m proud of my heart. It’s been played, burned, and broken, but it still works. 我为自己的心感到骄傲。它曾受玩弄，曾经心焦，曾遭破碎，却依然鲜活跳动。 If you don’t understand my silence, you will never understand my words.—如果你不懂我的沉默，你也永远不会明白我说的话语。 When life gives you a hundred reasons to cry, show life that you have a thousand reasons to smile.—当生活给你100个伤心的原因，你就还它1000个微笑的理由。 Learn to use the understanding of the vision to see and appreciate each other, in order to opinionated care to pipe each other.—学会用理解的，欣赏的眼光去看对方，而不是以自以为是的关心去管对方。 Whatever with the past has gone, the best is always yet to come.—无论过去发生过什么，你要相信，最好的尚未到来。 If we can only encounter each other rather than stay with each other, then I wish we had never encountered.—如果只是遇见，不能停留，不如不遇见。 There will be a tear that lets you grow in a twinkling.总会有一次流泪，让我们瞬间长大。 You are so lucky, because you can choose to love me or not, but myself only have to choose from loving you or loving you more.—你是幸运的，因为你可以选择爱我或不爱我，而我只能选择爱你还是更爱你。 Sometimes, you just have to pretend that you are happy just to stop everyone from asking you what the hell happened—有时候，你不得不假装很快乐，只是为了不让别人问“你怎么了？”Try to hold the right hand with your left hand, and gave yourself most simple warmth. We should learn to get it by ourselves instead of craving for warmth from others.试着用左手握住右手，给自己最简单的温暖，不再奢求别人的给予，开始学着自己给自己。 Among those people that appear in our life, some are to teach us, some to comfort us, some to share and some to love. 在我们生命中出现的人，一些给我们上课，一些让我们痊愈，有的用来分担分享，有的用来真爱。 等翻译：我喜欢你。是一句藏在心里很久的话。你可以不用回复我，但是，我却必须把它告诉你。 I’m proud of my heart. It’s been played, burned, and broken, but it still works. 我为自己的心感到骄傲。它曾受玩弄，曾经心焦，曾遭破碎，却依然鲜活跳动。 I don’t think that when people grow up.Conversely, I think it’s a selecting process, knowing what’s the most important and what’s the least. And then be a simple man.—人的心智成熟是一个逐渐剔除的过程，知道自己最重要的是什么，知道不重要的东西是什么。而后，做一个纯简的人。 Forget all the reason why it won’t work and believe the one reason why it will. ------ 忘掉所有那些“不可能”的借口，去坚持那一个“可能”。 Best way to not get your heart broken, is pretend you don’t have one.—不想伤心最好的办法就是假装自己没心没肺。 Memory is a wonderful thing if you don’t have to deal with the past。回忆本来是非常美好的，只要你能让过去的都过去 I‘d rather love someone I can‘t have than have someone I can‘t Love 。我宁愿爱上一个我不能拥有的人，也不想拥有一个我无法爱上的人。 There is still a long way to go. You may cry, but you have to keep on moving and never stop.前面
大数据时代信息安全面临的挑战与机遇 2013-7-11 10:17:00来源：中国科技网 根据有关学者的研究，数据密集型科学将成为继实验科学、理论科学、计算机科学之后，人类科学研究的第四个范式。以大数据为代表的数据密集型科学将成为新一次技术变革的基石。随着数据的进一步集中和数据量的增大，对海量数据进行安全防护变得更加困难，数据的分布式处理也加大了数据泄露的风险，信息安全正成为制约大数据技术发展的瓶颈。 大数据时代已经到来 物联网、云计算、移动互联网等新技术的发展，使得手机、平板电脑、PC及遍布地球各个角落的传感器，成为数据来源和承载方式。据估计，互联网上的数据量每两年会翻一番，到2013年，互联网上的数据量将达到每年667EB（1EB=230GB）。这些数据绝大多数是“非结构化数据”，通常不能为传统的数据库所用，但这些庞大的数据“宝藏”将成为“未来的新石油”。 1.大数据具有四个典型特征 大数据（Big Data）是指“无法用现有的软件工具提取、存储、搜索、共享、分析和处理的海量的、复杂的数据集合”。业界通常用四个V来概括大数据的特征。 ——数据体量巨大（Volume）。到目前为止，人类生产的所有印刷材料的数据量是200PB（1PB=210TB），而历史上
全人类说过的所有的话的数据量大约5EB（1EB=210PB）。当前，典型个人计算机硬盘的容量为TB量级，而一些大企业的数据量已经接近EB量级。 ——数据类型繁多（Variety)。这种类型的多样性也让数据被分为结构化数据和非结构化数据。相对于以往便于存储的以文本为主的结构化数据，非结构化数据越来越多，包括网络日志、音频、视频、图片、地理位置信息等，这些多类型的数据对数据的处理能力提出了更高要求。 ——价值密度低（Value）。价值密度的高低与数据总量的大小成反比。以视频为例，一部1小时的视频，在连续不间断的监控中，有用数据可能仅有一两秒。如何通过强大的机器算法更迅速地完成数据的价值“提纯”，成为目前大数据背景下亟待解决的难题。 ——处理速度快（Velocity）。这是大数据区分于传统数据挖掘的最显著特征。根据IDC的“数字宇宙”报告，预计到2020年，全球数据使用量将达到35.2ZB（1ZB=210EB）。在如此海量的数据面前，处理数据的效率就是企业的生命。 2.大数据成为国家和企业的核心资产 2012年瑞士达沃斯论坛上发布的《大数据大影响》报告称，数据已成为一种新的经济资产类别，就像货币或黄金一样。奥巴马政府已把“大数据”上升到国家战略层面，2012年3月，美国宣布投资2亿美元启动“大数据研究和发展计划”，借以增强收集
二、存在的主要问题及应对措施 1、我队干部职工的安全意识有待进一步提高。 重视和实现安全生产，必须有强烈的安全意识。随着社会的进步，“安全发展”成为社会共识，通过近年来的宣传和教育，我队干部职工的安全意识有所提高，但与我队的发展水平和管理要求仍有相当大差距，由于各种原因，部分干部职工的安全意识相对淡薄，安全生产并不能得到普遍和高度的重视。 提高我队干部职工的安全意识主要从以下两方面入手： 一是加强安全知识教育。要在队安委会、安全员例会、二级单位综合例会等会议上就安全政策法规、管理知识、标准规范等开展针对性的学习和贯彻。按要求做好各级安全教育培训工作。 二是搞好安全文化建设。结合我队文化建设，通过经常性的、宣传、教育、竞赛等多种形式的安全文化活动，引导全队从业人员的安全态度和安全行为，逐步形成为全体员工所认同、共同遵守、带有本我单位特点的安全价值观，实现法律和单位监管要求之上的安全自我约束。 开展系统而针对性安全教育和安全文化建设，是做好安全生产工作的基础。 2、安全规章制度、操作规程有待进一步补充和完善，必要的规范标准急需建立。 从系统性原则看，我队现有的规章制度、操作规程还不够完整、严密，更谈不上标准化和规范化。由于就规章制度、操作规程针对相关人员缺乏有效培训和学习,故在实际管理和生产中难以发挥应有的作用。 标准是对要求的细化和落实，是具体的要求、具体的指标。
有了标准，就可以规范人的行为、设备设施的状态、环境条件及管理的细节。我队目前缺乏操作性和实用性较强安全相关的标准。 我队安全工作的重点，一是勘察施工安全，二是消防安全，三是用车安全，对这几方面安全管理作进一步细化和规范并做到标准化，是下一步安全管理工作的重点。 安全生产，建章立制在先，通过全员参与，建立健全符合单位实际的制度、规程和标准，组织相关人员学习培训，实践中有效运行，及时反馈运行中的问题并修订，形成良性循环。 3、安全检查不够系统和严密，检查质量不高，检查未能做到标准化。 我队安全检查工作抓得较紧，领导重视，多次带队检查，安全管理人员常态化安全检查。但检查多依靠个人的经验和能力，检查质量难以保证，因此就检查情况发出的指令往往不够坚决，整改措施、要求不够具体和量化，整体效果也就不甚理想。 安全检查前应先对检查对象加以剖析，列出各层次的不安全因素，制成安全检查表，根据检查表逐项核对，查找不足，这样就能将个人的行为对检查结果的影响减少到最小。 无论是设计安全检查表还是制定标准，一项基础工作就是找出不安全因素，要发动全队职工针对各单位、各专业、各岗位开展查找危险有害因素活动，在此基础上制定安全检查表，同时，查找危险有害因素活动的过程，也是全员安全知识学习、安全意识提高的过程。 安全检查人员安全知识（特别是安全技术）的欠缺，是影响安全检查质量的重要原因。下一步将针对我队所涉及的钻探、建筑施工、临时用电、消防等安全技术加强学习。
大数据安全“互联网+”的核心挑战 北京市朝阳区法院曾经审理过一起借助“静默插件”盗取手机、计算机系统中的数据、非法控制计算机系统的案件。在这起案例中，引起重视的并不是插件破坏了用户的手机信息系统或者非法盗取用户的线上财富，而是在用户没有察觉的情况下将手机用户位置、存储卡信息、收发短信和通信录等关键信息上传到服务器或者网络空间，从而达到偷取、篡改用户数据的目的。据警方数据显示，该“静默插件”已经获取了超过了2000万条手机通信录，造成了真正的数据 安全威胁与挑战。 DT时代的二元挑战 阿里巴巴董事局主席马云曾提出，互联网正处于从IT向DT（数据技术）全面转型的时代。作为国内最大的电子商务集团，阿里巴巴其实某种意义上是一家数据公司，是将信息流、物流和资金流整合为三驾马车的数据创新型公司。 2015年是中国大数据发展高峰期，这一年我国政府部门颁布了大数据开放行动的战略。2015年底，《中共中央关于 制定国民经济和社会发展第十三个五年规划的建议》通过并提出了发展“互联网+”、分享经济和大数据等创新战略，更是将大数据开放、开发提到了国家战略高度。
可以预见，跨越2016年～2020年的“十三五”期间， 以大数据、云计算为特征的网络信息经济将成为中国ICT行 业发展方向。2015年热门的互联网+关键词更是集中智能化、融合与连接，不仅设备与设备相连，人与设备，人与人也会相连，万物互联（IOE）成为重要趋势。 在大数据获得开放的同时，也带来了对数据安全的隐忧。“数据安全是‘互联网+’时代的核心挑战，安全问题的特 征是线上和线下融合在一起的，“互联网+”时代产生的数据在存储、使用上和传统的方式有很大的区别，应急相应的黄金时间越来越短，信息位置越来越重要，要充分建立技术、人才和产业优势来获得我国在信息安全领域的优势性地位。”阿里巴巴集团安全部技术副总裁，原国家互联网应急中心副总工程师杜跃进博士说。 “从个人隐私安全层面看，大数据将网络大众带入到开放透明的裸奔时代，数据安全若保护不利，将引发民情抱怨不满”。中国信息安全测评中心研究员磨惟伟表示，针对大 数据时代的冲击，美英法日等主要国家已纷纷制定出相关战略，例如制定数据开放行动计划，加快数据立法安全保障体系建立。我国应该更加注重顶层设计发展与安全的双向并行，责任与担当双重并重，进而实现大数据发展运筹帷幄。 如何看待开放与安全这样一个二元化的挑战，中科院大学管理学院教授吕本富表示，“大数据的开放与安全问题目
1.I love three things in this world. Sun, moon and you. Sun for morning, moon for night , and you forever.予独爱世间三物。昼之日，夜之月，汝之永恒。 2.Life has taught us that love does not consist in gazing at each other, but in looking outward together in the same direction. 生活教会我们，爱并不在于长久地凝视，而在于眺望远方同一方向的希望。 3.Life isn't about waiting for the storm to pass, it's about learning to dance in the rain.生活不是等待暴风雨过境，而是学会在雨中跳出最美的舞姿。 4.You know my loneliness is only kept for you, my sweet songs are only sung for you.你可知我百年的孤寂只为你一人守侯，千夜的恋歌只为你一人而唱。 5.If living on the earth is a mission from the lord…living with you is the award of the lord…如果活着，是上帝赋予我最大的使命，那么活者有你，将会是上帝赋予我使命的恩赐…… 6.Do you understand the feeling of missing someone? It is just like that you will spend a long hard time to turn the ice-cold water you have drunk into tears.你知道思念一个人的滋味吗，就像喝了一大杯冰水，然后用很长很长的时间流成热泪。 7.In such a soft and warm season, please accept my sincere blessing and deep concern for you.在这充满温馨的季节里，给你我真挚的祝福及深深的思念。 8.For our ever-lasting friendship, send sincere blessings and warm greetings to my friends whom I miss so much.一份不渝的友谊，执着千万个祝福，给我想念的朋友，温馨的问候。 9.It is graceful grief and sweet sadness to think of you, but in my heart, there is a kind of soft warmth that can’t be expressed with any choice of words.想你，是一种美丽的忧伤的甜蜜的惆怅，心里面，却是一种用任何语言也无法表达的温馨。 10.You and I remains the same in different time, at different places,among different people; time is changing, space is changing and everything is changing except my miss to you!不同的时间，不同的地点，不同的人群，相同的只有你和我；时间在变，空间在变，不变的只有对你无限的思念！ 11.Coffee is lonely without cups just as I am lonely without you.没有杯子……咖啡是寂寞的……没有你……我是孤独的…… 12.My heart beats for you every day. I am inspired by you every minute, and I worry about you every second. It is wonderful to have you in my life.每一天都为你心跳，每一刻都被你感动，每一秒都为你担心。有你的感觉真好。 13.No matter the ending is perfect or not, you cannot disappear from my world.我的世界不允许你的消失,不管结局是否完美. 14.Love is a carefully designed lie.爱情是一个精心设计的谎言. 15.Promises are often like the butterfly, which disappear after beautiful hover.承诺常常很像蝴蝶,美丽的飞盘旋然后不见 16.Fading is true while flowering is past凋谢是真实的,盛开只是一种过去 17.Why I have never catched the happiness? Whenever I want you ,I will be accompanyed by the memory of...为什么幸福总是擦肩而过,偶尔想你的时候….就让….回忆来陪我. 18.Love ,promised between the fingers.Finger rift,twisted in the love爱情…在指缝间承诺指缝….在爱情下交缠. 19.If you weeped for the missing sunset,you would miss all the shining stars如果你为着错过夕阳而哭泣，那么你就要错群星了 20.To feel the flame of dreaming and to feel the moment of dancing,when all the romance is far away,the eternity is always there.感受梦的火焰，感觉飞舞瞬间，当一切浪漫遥远，永恒依然
Top Ten Big Data Security and Privacy Challenges November2012
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工作汇报/工作计划/安全工作总结 姓名：____________________ 单位：____________________ 日期：____________________ 编号：YB-ZJ-060973 安全工作方面存在的问题总结Summary of problems in safety work
安全工作方面存在的问题总结 一、全国安全生产形势的主要特点 我国是个大国，安全生产问题突出。全国现有2.6万处煤矿，10.2万处非煤矿山;有汽车2830万辆，加上摩托车、农用车辆，机动车保有量1.1亿辆; 7万多座加油站，1.44万处铁路与公路交叉道口，每天1万多架次民航起落飞机，每天150多万艘大小船只在内河和海上飘泊行驶。据近年来的统计数据分析，每年全国因为各类事故死亡10万多人，伤残70多万人，加上职业危害，一年就有近百万的家庭因为安全生产事故造成了不幸。其主要特点是：一是事故总量下降。上世纪九十年代之后，随着工业化、城市化进程加快和社会生产规模急剧扩大，我国开始进入新一轮事故高发期，事故死亡人数连年增加，20xx年达到139393人的历史点。经过努力，与20xx年相比，亿元GDP 事故死亡率从1.33降到了20xx年的0.413，十万工矿商贸从业人员事故死亡率从4.05降到3.05，道路交通万车死亡率从13.7降到了5.1，煤矿百万吨死亡率从4.94降到了1.485。20xx年全国事故起数和死亡人数分别下降19.3%和10.1%。 二是煤矿事故逐年下降。20xx—20xx年，煤矿事故平均每年减少350起、562人，年均降幅8%。20xx年发生煤矿事故2945起、死亡4746人，比上年
I missed you but I missing you. I missing you but I missed you .I see you but I seeing you . I seeing you but I see you.------明明已经错过你，但我却还在想念你。当我想念你的时候，但我 不能再拥有你。明明已经别离，却又再次相遇。当我们再次相遇时，却不得不说再见 ?
Some disappointment is inevitable, but most of the disappointed, because of you overestimate themselves. 有些失望是不可避免的，但大部分的失望，都是因为你高估了自己。 ?
Can I don't have a boyfriend, and don't have money, but I can't do without you 我可以没有男朋 友，没有钱，可是我不能没有你 ?
Sometimes I wish I could just fast forward time just to see if in the end it’s all worth it.---有时 候，我真希望我能快进时间，这样我就能看看，最终的结果是不是值得。 ?
I love you not for who you are, but for who I am before you. 我爱你不是因为你是谁，而是我在 你面前可以是谁。 ?
Guys use the word “friendship” to start a relationship. Girls use the word “friendship” to end it.男 人喜欢用“友情”开始一段爱情，女人喜欢用“友情”终结一段爱情。 ?
If one really cares for you,he is able to squeeze time for you.No excuses,no lies,no undeliverable promises. 如果一个人真的足够在乎你，那么他总能挤出时间来陪你，没有借口、谎言，没
大数据金融的风险与挑战分析 发表时间：2019-06-18T17:05:39.990Z 来源：《基层建设》2019年第8期作者：俞翔[导读] 摘要：金融企业的长期发展中积累了大量有价值的数据，而大数据金融的发展也是趋势，为能应对金融领域的激烈竞争发展环境，就要充分注重精细化运营，降低运行成本，促进金融大数据的良好发展。 联通（浙江）产业互联网有限公司浙江省杭州市 310051摘要：金融企业的长期发展中积累了大量有价值的数据，而大数据金融的发展也是趋势，为能应对金融领域的激烈竞争发展环境，就要充分注重精细化运营，降低运行成本，促进金融大数据的良好发展。大数据金融的发展中并不是一帆风顺的，所面对的问题也比较多，这就需要从多方面加强重视，积极应对大数据金融风险。 关键词：大数据金融；风险；挑战 1大数据金融发展面临的挑战大数据金融的实际发展过程中，所面临的挑战比较多，如数据共享平台的缺乏。金融机构数据信息都比较保密，金融机构间的联系没有加强，数据不能有效共享，有着不能、不愿、不敢共享的现状，金融需求很难得到有效满足。金融机构为能和客户建立良好关系，就会增加金融活动成本，以及增加风险，数据共享平台的缺乏，使得金融机构很难满足海量用户个性化需求，这是发展当中的一个挑战。大数据金融发展所面临的挑战还体现在，数据基础设施的承载能力比较薄弱上，大数据基础设施支撑大数据应用和产业的发展，大数据金融是完整链条，其中有诸多的中间环节，每个环节也都有独立机构需要经营管理，每个环节也比较容易产生大量数据往来，各环节的基础数据设施支撑面临很大冲击，基础设施承载力的强化就显得比较重要，而在这一方面还存在薄弱环节。除此之外，大数据金融发展的挑战还体现在专业人才缺乏上，大数据发展对复合型人才比较注重，需要有各领域的知识技能，如计算机技术以及统计学和数学等领域知识。金融业在大数据方面的专业人才缺乏的问题还比较突出，发展大数据金融就要先从人才储备方面加强重视，这是未来发展中所面临的一个重大挑战。 2大数据金融的风险从大数据金融发展中所存在的风险来看，体现在诸多层面，有以下几个方面：其一，数据监听风险。大数据金融的发展中，如果是数据监听就会对国家的金融安全造成很大影响，从实际的案例当中能了解到，在2013年的“棱镜门”事件当中，海量数据加数据挖掘大数据监听模式能精确监听，所以因这一事件的出现就停止了和美国共享金融数据。我国的大数据发展的速度也比较迅速，但是软硬件设施以及数据服务都比较依赖国外，所以这就必然存在着数据监听以及泄漏的风险，对我国金融安全的发展就会产生很大的威胁。其二，侵犯隐私风险。当前大数据金融的发展是比较迅速的，数据的应用侵犯客户个人隐私是不争的事实，大数据技术的应用中传递技术以及超强计算机系统，对数据分析比较高效，交叉检验技术以及块数据技术的应用，在大数据的身份识别方面比较简单化，大数据金融要对客户信息全面分析应用，而正是在这样的情况下就会不知不觉的侵犯客户的信息隐私。其三，法律监管风险。大数据金融由于是新兴的行业，尤其是近些年的发展速度比较快，但是相应的法律制度没有与之相同步，这就会由于法律上的缺失以及不完善等，造成监管风险。大数据金融的涉及面是比较广的，和当前监管体系不能有效重合，在具体的管理过程中就会存在着很大难度，从而也会造成大数据金融风险。操作当中有的P2P通过集合理财方式获得大量活期或者是定期理财资金，采用智能投标把理财资金以及资产错配，然后把投资债权在平台转让其他投资人。由于在监管的标准有着不同，当前法规对于网贷平台内部投资的债权转让没有明确规定，所以监管处在真空地带。 3大数据金融的发展的策略实施 3.1建立担保机制 大数据金融创新可能存在巨大漏洞，比如我国P2P网络贷款平台已经显现弊端。部分P2P网络平台并没有将客户资金和平台资金有效分开，出现了若干平台的负责人携款跑路的现象。针对这种大数据金融资金安全问题，具体方案可参照淘宝模式，要求各大平台做好贷款交易的中介媒体，将平台自有资金和客户资金区分开，再由平台垫付筹资者的使用资金，起到担保作用，并建立客服中介系统。如此，可使平台自发重视借款人的信用平价、还款能力等真实信息，确保资金的安全。并同时要求平台严格保护客户私人信息及贷款交易信息，以备事后追责，并作出保证不在投资人权限外利用客户信息，以此来保证信息安全。 3.2完善金融法的立法内容 很多发达国家的早期适合性原则在被金融危机冲击过后，已显得不是那么严谨了，因此有很大完善的空间。对于金融交易中资产组合的风险分散与配置的问题上，立法上已经更倾向于关注金融资产与投资者最大化收益和可能承受最大风险上来了。不论传统金融还是大数据金融，立法都应趋于一致。大数据金融最大的缺陷在于在平台上是由平台系统自动给投资者分配投资项目，而不是投资者自己进行选择。这就使得当投资项目的收益与个人可能承担最大风险不相匹配，导致项目出现大的波动的时候，容易造成投资者的恐慌，以致于大规模撤回资金，使得平台瘫痪，甚至破产等一系列连锁反应。因此在完善立法的同时，还要要求平台充分披露信息和揭示风险，保障客户的知情权和选择权，不能宣传发布虚假或具有误导及诱导的信息，以此来消减信息不对称的程度，促使投资者可以进行更客观理性的判断，选择最适合自己的投资方案。 3.3建立大数据金融监管机制 为了降低监管风险，应该建立严格的大数据金融监管机制，避免大数据监管泛泛化，确实做到专业化、及时化、准确化。由于大数据分布广阔，金融交易具有极大的灵活性，大数据将金融交易范围极限扩大，更好的完成资源配置，同时也将监管难度升级，此时分类监管可以帮助提升监督效率，可以按大数据金融的业务模式进行分类，区别监管，确定监管的对象、监管的范围，对于大风险、大规模的金融资产的交易，区分是该采用原则性监管还是限制性监管。在监管金融交易的同时，要注意各个监管机构的相互协调与配合，加强信息技术非现场检查，建立风险预警机制和应急处理机制。我国的大数据金融有别于其他发达国家的地方在于线上的个人征信系统不完善，需要在线下进行调查，成本高效率低，相当于大规模的线上民间借贷。而且，无论是在机构数量上还是促成的借贷金额上，都远超于其他国家，鱼龙混杂，风险事件频发，因此需要对整个过程进行严格监管。比如准入资格监管，对借款人的经营条件、经营范围，管理层的设定等方面给出确定的标准；对运行过程进行监管，让网络化金融透明化等。尽管金融已实现大数据化，但其并没有脱离金融的本质，因此要对传统金融和大数据金融的监管达到一致，协调统一。 4结语
关于做好当前安全生产工作的几点建议 一、关于当前安全生产工作的形势 今年度以来，我街道相继发生多起建筑施工领域亡人事故，事故总量和事故危害程度严重上升，给街道全年安全生产工作带来了较为被动的局面，但这并不能成为我们对安全生产工作懒散、怠慢的理由，反而是给我们的一种警示，警示我们要进一步强化“红线”意识、责任意识、守法意识和服务意识，提高安全生产工作的自觉性。要在制度执行、网格建设、吸取事故教训、加强分包队伍管控、提升突发事件处置能力等5个方面下功夫，全面开展未遂事故、轻伤事故的统计分析，找出事故规律，摸清事故苗头和倾向，把事故遏制在萌芽状态。 二、存在的问题 一是安全生产基础不到位。相对于其他工作，安全工作是较为新的一项工作，在外国全面抨击中国不重视人权的背景下，国家安全生产监督管理总局于05年才独立成为正部级单位，所以工作历史欠账较多。另外一些行业、领域和地区安全状况比较严峻，生产装置、设备老化，加之维修和更新改造不落实，管理混乱，安全保障能力低下。再加上很多企业长期安全投入不足，基础安全设施更是严重投入不足。一些行业存在大量不符合安全生产条件的小企业，点多面广，治理难度大,相当部分企业的安全生产条件仍达不到国家规定要求，安全科技进步进展不快。 二是思想认识不到位。为什么会有安全生产基层基础不到位?就是因为管安全的、从事安全的、有安全生产监管责任的人的思想认识出了问题。在追求经济高速增长的时代背景下，不惜一切代价换取GDP的做法自上而下。安全给政绩让路、安全给效益让路的现象依然严重，把一切精力用在片面追求任务的完成、效益的最大化上，甚至牺牲从业人员的生命和健康追逐利润。在全面推行安全生产网格化管理的背景下，不少社区反映部分干部确实有点“喊不动”。 三是从业人员安全意识不到位。从业人员安全生产防范意识较为淡薄，缺乏必要的安全生产知识、安全操作技能和自我保护能力，存在冒险蛮干、违章作业的现象，安全意识和安全生产法规观念仍然比较淡薄。 四是严格检查执法不到位。按照《中华人民共和国安全生产法》第五十九条的规定县级以上地方各级人民政府应当根据本行政区域内的安全生产状况，组织有关部门按照职责分工，对本行政区域内容易发生
1、Happiness can be found even in the darkest of times. 即使在最黑暗的日子里，也能寻到幸福。 2、Hand and catch you fell out of the tears, but not cut the fund us you伸手，接住你眼角垂落的泪滴，却截不住你眼底的悲伤！ 3、Follow your heart, but be quiet for a while first. Learn to trust your heart. 跟着感觉走，静静地。学着去相信自己的内心。 4、First I need your hand ,then forever can begin——我需要牵着你的手，才能告诉你什么是永远 5、Fallen into the trap,for you are too greedy,it's not because of others' cunning.会上当，不是因为别人太狡猾，而是因为自己太贪. 6、Fake friends never betray in front of you. They always do it behind you. 假朋友从不会当面背叛你，都是背后。 7、Fading is true while flowering is past凋谢是真实的,盛开只是一种过去 8、Every time you come to mind, I realize I'm smiling. 每次想到你，我就发现自己是微笑着的。
9、Everything happens for a reason 这个世界，没有偶然。 10、Everyone has problems. Some are just better at hiding them than others. 每个人都有自己的难处，只不过有的人不容易被发觉罢了。 12、I can put the past you're so natural, I think I really feel. 我可以把过往的你说得如此自然，我想我真的释怀了。 13、Be nice to people on the way up, because you'll need them on your way down.向上爬时，对遇到的人好点，因为掉下来时，你还会遇到他们。 14、Be happy. It’s one way of being wise. 做个快乐的人。那是英明智慧的一条路径。 15、As long as it is a comedy, I’d rather cry during the process. 只要是个喜剧结局，过程你让我怎么哭都行。 16、Always listen to your heart because even though it's on your left side, it's always right.总是听从内心的声音。因为即便它长在你的左边，它却总是对的。